Citations

Abstract Surgery continues to be a mainstay of treatment for patients with symptomatic fibroids. Hysterectomy is an option for those desiring definitive treatment and myomectomy is preferred for those that have not yet completed childbearing or wish to retain their uterus. While open laparotomy remains a viable approach to both hysterectomy and myomectomy, minimally invasive techniques for the surgical management of symptomatic fibroids are preferred options for many due to improved patient outcomes. These include hysteroscopic myomectomy, laparoscopic and robotically assisted myomectomy, and vaginal, laparoscopic, and robotically assisted hysterectomy. Patients have a faster recovery and less postoperative pain compared to a laparotomic approach. More recently, uterine‐sparing outpatient modalities such as ultrasound‐guided radiofrequency ablation and high‐intensity focused ultrasound have emerged as promising alternatives for selected patients. Benefits include clinically significant reduction in fibroid size and menstrual bleeding with small or no abdominal incisions and an expedited convalescence. This article reviews preoperative evaluation and imaging for fibroids, as well as the techniques, approach, indications, patient selection, procedural management, and surgical and fertility outcomes for each surgical and procedural management of uterine fibroids.

Artificial intelligence (AI) is now applied across oncology, including imaging, pathology, therapy selection, toxicity assessment, and survival modeling, and large language models (LLMs) have been adopted particularly quickly. Reported physician use of AI roughly doubled between 2023 and 2026. The frameworks used to evaluate these tools have not advanced at the same rate. Many are available, but each was designed for a single stage of the AI lifecycle, and none directly answers the question most relevant to a treating clinician: whether a given tool is appropriate for the individual patient under care. We reviewed the frameworks in current use, including TRIPOD+AI, PROBAST+AI, CLAIM, SPIRIT-AI, CONSORT-AI, DECIDE-AI, MINIMAR, and CREMLS, together with the oncology-specific ESMO EBAI and the newer LLM-specific guidance (TRIPOD-LLM, MI-CLEAR-LLM, CHART, and ESMO ELCAP). Each addresses part of the evaluation problem, but none is sufficient on its own for oncology, where the standard of care changes rapidly, assays drift, biomarker-defined subgroups are small, and a model validated in one period may perform poorly in the next. LLMs introduce additional challenges, including sensitivity to prompting, undisclosed vendor updates, behavioral drift, and hallucination rates approaching 50% on clinical quality-assurance tasks. The predictive-model frameworks were not designed to capture these failure modes. The central argument of this review is that the frameworks the field requires already exist; what is missing is their mandatory adoption. We propose that journals and regulators move from recommending these frameworks to requiring their use, and we outline a cancer-aware evaluation pathway together with the specific responsibilities of authors, reviewers, journals, and regulators in implementing it.

Background: Colorectal neuroendocrine carcinomas (NECs), including small cell (SCNEC) and large cell (LCNEC) subtypes, are rare but aggressive malignancies with limited population-level data. Moreover, prior studies have limited stratification by histologic subtype and anatomic location, despite evidence of disease heterogeneity in colorectal malignancies. This study aimed to characterize the clinical characteristics, treatment patterns, and outcomes of colorectal NEC by histologic subtype and tumor location, and to compare them with neuroendocrine tumors (NETs) and adenocarcinomas (ACs). Methods: A retrospective cohort review was conducted utilizing the SEER database from 2000 to 2022 to identify patients with colorectal SCNEC, LCNEC, NET, and AC. Demographic, clinical, therapeutic and survival data were analyzed and compared using Kaplan–Meier methods, log-rank tests, and Cox proportional hazards regression. Results: A total of 790 SCNEC, 498 LCNEC, 31,200 NET, and 638,898 AC cases were identified. SCNEC and LCNEC were associated with advanced stage at diagnosis (66% and 55% stage IV, respectively) and inferior survival outcomes compared to NET and AC (median OS (mOS) of 7 and 8 months for SCNEC and LCNEC, respectively, p = 0.0002). Stratified by location, colonic SCNEC had the poorest survival (mOS 5 months), worse than rectal SCNEC (mOS 9 months); this pattern was not observed in LCNEC. Surgical resection was associated with improved OS in both NEC subtypes (HR range 0.25–0.56, all p < 0.012). Radiation therapy was associated with improved survival in NEC overall (SCNEC HR 0.58; LCNEC HR 0.71, both p < 0.05), with the observed benefit appearing greatest in rectal NEC. Demographic factors were associated with survival in NET and AC but had no significant impact in NEC. Conclusions: Colorectal SCNEC and LCNEC are highly aggressive malignancies with poor outcomes, with SCNEC demonstrating modestly inferior outcomes compared to LCNEC. This study demonstrates that both histologic subtype and anatomic site of disease are important determinants of prognosis and treatment response. This supports the concept that colorectal NEC represents a biologically heterogeneous disease, with colonic SCNEC showing the worst outcomes and rectal NEC associated with a potential enhanced response to radiation. These findings support the need for further prospective and molecular studies to better define treatment approaches and targeted therapies for these rare malignancies.

Introduction Uveal melanoma has a marked tropism for the liver where immune tolerance facilitates metastatic progression and resistance to immunotherapy. Impaired CD8 + T cell immunosurveillance is a central determinant of disease progression in uveal melanoma, yet the underlying mechanisms driving liver-mediated immune subpression and hepatic metastasis are not fully understood. Here, we investigate molecular pathways that regulated hepatic CD8 + T cell function and evaluate clinical outcomes associated with liver-directed therapy. Methods Expression of autotaxin (ATX), the expression responsible for generating lysophosphatidic acid (LPA), was evaluated in hepatic resident cell populations. ATX transcript levels were assessed in CD8 + T cells isolated from hepatic metastases and compared with matched peripheral blood and healthy donor control. Antigen-specific effector CD8 + T cells were used to characterize downstream signaling responses to LPA stimulation. Functional studies evaluating cytokine production during chronic antigen exposure were performed using CD8 + T cells from LPA receptor 5-deficient ( Lpar5 -/- ) mice. Results We identify the autotaxin-lysophosphatidic acid (ATX-LPA) axis as a prominent immunoregulatory pathway in the metastatic hepatic tumor microenvironment. ATX, the enzyme responsible for LPA production, is constitutively expressed by hepatic resident cells, is detectable on Kupffer cells and is associated with increased immunosubpressive markers. CD8 + T cells isolated from hepatic metastasis in a patient with uveal melanoma display markedly elevated ATX transcripts compared to matched peripheral blood (&gt;5 log 2 fold change) and healthy controls. Using antigen-specific effector CD8 + T cells, we demonstrate LPA signaling induces low level, persistent ERK phosphorylation distinct from canonical antigen-induced TCR signaling. Genetic deletion of the LPA receptor 5 ( Lpar5 -/- ) on CD8 + T cells restores CD8 T cell percentages and cytokine function during persistent antigen stimulation in the presence of LPA. Discussion These findings define an ATX-LPA mechanisms of dysfunctional ERK signaling that may contribute to hepatic immune subpression in uveal melanoma. We highlight this pathway as a rational therapeutic target alongside liver-directed clinical interventions.

ABSTRACT Overview Systemic mastocytosis (SM) results from clonal proliferation of mast cells (MC) in extra‐cutaneous organs. Diagnosis The major criterion is the presence of multifocal MC clusters in the bone marrow and/or extracutaneous organs. Minor diagnostic criteria include elevated serum tryptase level, MC CD25/CD2/CD30 expression, and the presence of activating KIT mutations. Risk Stratification Establishing SM subtype as per the International Consensus Classification/World Health Organization classification systems is an important first step. Patients either have indolent/smoldering SM (ISM/SSM) or advanced SM, including aggressive SM (ASM), SM with associated myeloid neoplasm (SM‐AMN), or mast cell leukemia. Identification of poor‐risk mutations (i.e., ASXL1, RUNX1, SRSF2, NRAS ) further refines the risk stratification. Several risk models are available to help assign prognosis in SM patients. Management Treatment goals for ISM patients are primarily directed towards anaphylaxis prevention/symptom control/osteoporosis treatment. Patients with advanced SM frequently need MC cytoreductive therapy to reverse disease‐related organ dysfunction. Tyrosine kinase inhibitors (TKI) (midostaurin, avapritinib) have changed the treatment landscape in SM. While deep biochemical, histological, and molecular responses have been documented with avapritinib treatment, its efficacy as monotherapy against a multimutated AMN disease component in SM‐AMN patients remains unclear. Cladribine continues to have a role for MC debulking, whereas interferon‐α has a diminishing role in the TKI era. Treatment of SM‐AMN primarily targets the AMN component, particularly if an aggressive disease such as acute leukemia is present. Allogeneic stem cell transplant has a role in such patients. Imatinib has a therapeutic role only in the rare patient with an imatinib‐sensitive KIT mutation.

Abstract The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently celebrated its 20th anniversary, reflecting two decades of major contributions to Alzheimer's research through open data sharing and longitudinal multimodal assessments. This review synthesizes 122 high‐impact studies using ADNI data or biospecimens from 2023 to mid‐2025 to clarify mechanisms of Alzheimer's disease (AD) progression. Studies describe impairment of glymphatic clearance and the impact of cerebral small vessel disease, trajectories of amyloid beta and tau deposition, inflammation, metabolic disturbances, synaptic dysfunction, and neurodegeneration, leading to cognitive impairment and neuropsychiatric symptoms. Multifactorial contributions from genetic and epigenetic influences, co‐pathologies and comorbidities, and mechanisms of resilience modulate disease progression. Finally, heterogeneity of clinical presentation and disease course is described in the context of multiple contributing factors, highlighting the complexity of AD. By integrating imaging, fluid biomarkers, genetics, and clinical measures, ADNI provides a comprehensive research dataset for unraveling mechanisms underlying AD progression.

We report the case of a previously healthy, 6-year-old boy who presented with 2 days of generalized body aches. Given a recent viral illness with cough and congestion, the emergency department’s initial diagnosis of the condition was viral myositis. He was admitted for pain treatment. During his hospitalization, he developed worsening pain, decreased mobility, and intermittent nocturnal desaturations. The team elicited additional history of snoring and witnessed apneas, which raised concern for obstructive sleep apnea. They consulted pulmonology who was concerned for central sleep apnea and respiratory depression and recommended consulting neurology. Neurology recommended a lumbar puncture, the results of which led to the eventual diagnosis. This case highlights the importance of critical reasoning and knowledge of the pediatric presentation of diseases that affect both adults and children.

Abstract Objective . Phase gating is a critical technique to mitigate tumor motion during radiotherapy, particularly in spot-scanned particle therapy where internal motion can interfere with dynamic spot scanning patterns and, simultaneously, introducing substantial range uncertainties. However, the current commercial state of the art in real-time phase prediction is challenged by patient-specific breathing variability as well as detection and delivery system latencies. This leads to suboptimal efficiency, mis-timed radiation delivery and requires frequent manual intervention. This study aims to improve phase prediction accuracy using deep learning (DL)-based time series forecasting to enable more accurate dose delivery. Approach . Retrospective breathing waveforms from 69 proton therapy patients, sampled at 30 Hz, were labeled with inspiratory peaks and assigned subjective regularity scores (four levels). DL models with various architectures were trained using waveform amplitude to predict current or future breathing phases. Model performance was evaluated using mean squared error, phase binning accuracy, and timing deviation for radiation on/off events. Bayesian optimization was used for hyperparameter tuning. Results were compared between models and against a commercial algorithm currently in clinical use. Main Results . The curated waveform dataset included 165 242 s for training, 24 057 s for validation, and 30 322 s for testing, with an additional 40 604 s from separate patients for extended validation. The long short-term memory and temporal fusion transformer models significantly outperformed the commercial algorithm, improving phase prediction accuracy by nearly 20% and reducing timing deviations across all regularity levels. Significance . DL-based time series forecasting may substantially improve breathing phase prediction accuracy over current clinically available methods, offering a more precise and reliable approach to phase-gated radiation delivery.